Wyniki wyszukiwania - Biblioteka Nauki

1

Wprowadzenie do programowania genetycznego. Cz.1 Elementy systemu programowania genetycznego

100%

Wieczorek W.

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1999

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tom Nr 6

8-13

PL

Na wiele problemów z dziedziny uczenia się oraz sztucznej inteligencji możemy spojrzeć jak na problem znajdowania programu, który dla określonych danych wejściowych wyznacza wymagane dane wyjściowe. Z tego punktu widzenia, proces rozwiązywania problemu zostaje sprowadzony do przeszukiwania przestrzeni wszystkich możliwych programów komputerowych w celu znalezienia właściwego programu.

2

Wprowadzenie do programowania genetycznego. Cz.2 Przykłady problemów rozwiązywanych za pomocą programowania genetycznego

80%

Wieczorek W.

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1999

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tom Nr 7-8

20-25

PL

Niniejsza praca jest kontynuacją części pierwszej, w której przedstawiono ideę programowania genetycznego oraz omówiono podstawowe elementy systemu programowania genetycznego. Obecnie na wybranych przykładach pokażemy, jak zdefiniować różne problemy tak, aby do ich rozwiązania można było użyć metody programowania genetycznego. Rozważymy trzy przykłady: projekt multipleksera, indukcję ciągu oraz problem zaminowanego obszaru.

3

Synteza impedancji dwójnika pasywnego RLC z wykorzystaniem algorytmu programowania genetycznego

70%

Kyzioł P. , Grzechca D. , Golonek T. , Rutkowski J.

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2010

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tom Vol. 51, nr 9

24-28

PL

Przedstawiona w artykule metoda pozwala na dobranie takiej struktury dwójnika RLC, która zrealizuje zadane wartości impedancji. Zaproponowane podejście syntezy dwójnika RLC wykorzystuje heurystyczny algorytm programowania genetycznego. Wykorzystanie algorytmów heurystycznych do problemu syntezy analogowych układów RLC nie jest nowym pomysłem. W literaturze są spotykane prace wykorzystujące algorytmy heurystyczne do syntezy funkcji układowych analogowych układów (np. charakterystyki amplitudowej czwórnika). Zaprezentowane wyniki badań pokazują, że otrzymana struktura RLC realizuje zadane punkty impedancji.

EN

The method of syntesis impedance of 2-terminal RLC network, using Genetic Programing algorithm is proposed in this paper. Examples of deterministic methods of synthesis RLC network have been described. The object of investigation is that heuristic algorithm can be used, for the problem of synthesis of circuit impedance. Simulation results has been presented for RLC structure that realize set of four impedance values.

4

Miary różnorodności populacji oparte na entropii w programowaniu genetycznym

60%

Adamczyk R.

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2008

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tom Vol. 29, nr 3A

51-69

PL

Artykuł przedstawia wyniki badań różnorodności populacji w programowaniu genetycznym z reprezentacją grafową. Celem głębszego zrozumienia warunków pomyślnej ewolucji algorytmów programowania genetycznego zbadano kilka miar różnorodności związanych z pojęciem entropii. Analizę korelacji miar z jakością wyników przeprowadzono za pomocą standardowych statystyk.

EN

This paper presents a study of population diversity in genetic programming with graph representation. Several measures of diversity based on entropy are considered to gain a deeper understanding of the conditions under which the evolution of genetic programming algorithms is successful. Standard statistics are used to analyze their correlation with performance.

5

Genetic programming for the prediction of tensile strength of cast iron

60%

Duda J. , Stawowy A.

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2011

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tom Vol. 11, iss. 4

31-34

EN

In this paper we propose genetic programming (GP) to predict tensile strength of ductile cast iron. The chemical composition and pouring temperature were used as explanatory input variables (parameters), while tensile strength as dependent output variable (response). On the basis of real data set collected in a one of the Polish foundries, two different models for output variable were developed by genetic programming. Statistical analysis of obtained results and two test cases were employed to compare the accuracy of the GP model with the neural network (NN) model and a linear multiple regression model. The comparison demonstrated that the GP outperforms regress ion techniques, while it is generally worse than NN. Nevertheless GP can be a powerful tool for predicting the mechanical properties of cast iron as it provides a mathematical model, which can be further analyzed.

6

Evolutionary computation framework for learning from visual examples

60%

Krawiec K.

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2001

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tom Vol. 7, no 3-4

85-96

EN

This paper investigates the use of evolutionary programming for the search of hypothesis space in visual learning tasks. The general goal of the project is to elaborate human-competitive procedures for pattern discrimination by means of learning based on the training data (set of images). In particular, the topic addressed here is the comparison between the "standard" genetic programming (as defined by Koza [13] and the genetic programming extended by local optimization of solutions, so-called genetic local search. The hypothesis formulated in the paper is that genetic local search provides better solutions (i.e. classifiers with higher predictive accuracy) than the genetic search without that extension. This supposition was positively verified in an extensive comparative experiment of visual learning concerning the recognition of handwritten characters.

7

Model reference-based machining force and surface roughness control

60%

Cus F. , Zuperl U.

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2008

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tom Vol. 29, nr 2

115-122

EN

Purpose: The main objective of this paper is to present the development of an empirical model-based control mechanism to maintain a fine surface finish quality by maintaining on-line cutting values. Design/methodology/approach: The proposed model has been developed to present the control model constraints, by varying the machining parameters to control the force output to be constant. Genetic programming method (GP) has been applied to derive empirical relationship of the surface finish and the cutting force. These relationships have been applied to develop the proposed simulation model, in which the cutting force is adjusted to improve the required surface finish for the end milling operation process. Findings: The experimental results show that not only does the milling system with the design controller have high robustness, and global stability but also the machining efficiency of the milling system with the adaptive controller is much higher than for traditional CNC milling system. Experiments have confirmed efficiency of the adaptive control system, which is reflected in improved surface quality and decreased tool wear. Research limitations/implications: The proposed architecture for determining of optimal cutting conditions is applied to ball-end milling in this paper, but it is obvious that the system can be extended to other machines to improve cutting efficiency. Practical implications: The results of experiments demonstrate the ability of the proposed system to effectively regulate peak cutting forces for cutting conditions commonly encountered in end milling operations. The high accuracy of results within a wide range of machining parameters indicates that the system can be practically applied in industry. Originality/value: By the GP modeling the system for adaptive adjustment of cutting parameters is built.

8

Model reference adaptive force and surface roughness control in milling

60%

Cus F. , Zuperl U.

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2008

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tom Vol. 26, nr 2

179-182

EN

Purpose: of this paper. The paper presents the model based mechanism of control assuring constant quality of surface finish by controlling the cutting forces in the end milling process. By dynamic adaptation of feeding and speed the system controls the surface roughness and the cutting forces on the milling cutter. The purpose of developing such a mechanism is to find the limitations of such control which maintains constant cutting force by adapting the cutting parameters. Design/methodology/approach: The model based system of control has been developed by the evolutionary method of genetic programming (GP). A drawing of experiments has been made in order to determine the empirical correlations between the quality of surface finish and the cutting force. Genetic programming method has been applied to derive empirical relationship of the surface finish and cutting force values for steel material. These relationships have been applied to develop the proposed evolution simulation model in which the cutting force is adjusted to improve the required surface quality. Findings: The system eliminates the problems related to assurance of quality of machining, efficiency of machining and prevention of tool damages. Research limitations/implications: While force control approach performed satisfactorily in a laboratory environment, it can be generally concluded that their implementation should be dictated by the economics of the production environment. Practical implications: The results provide a means of greater efficiency by improving the surface quality, minimizing the effect of the process variability and reducing the error cost in finishing operations. Originality/value: An adaptive system of control which controls the cutting force and maintains constant roughness of the machined surface during milling by continuous dynamic adjustment of the cutting parameters is devolped.

9

Składowe oceny animacji wirtualnych postaci ludzkich

60%

Lach E. , Chaja T.

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2012

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tom Vol. 33, nr 1

47-58

PL

W artykule zaprezentowano składowe oceny animacji wirtualnych postaci ludzkich, które mają za zadanie wykrycie i usunięcie widocznych defektów obserwowanych w automatycznie generowanych animacjach. Składowe oceniają realizm animacji. W artykule zostały także przedstawione eksperymenty, które wykazały, że użycie proponowanych składników zwiększa realizm animacji generowanych za pomocą programowania genetycznego.

EN

The paper proposes evaluation methods for virtual humans behaviors, which are generated automatically. Presented methods measure realism of an animation. Experiments are described, which generate animations by means of genetic programming. Obtained results show that using proposed methods produces more realistic animations.

10

Analytical study of different approaches to determine optimal cutting force model

41%

Krizek Z. , Jurkovic Z. , Brezocnik M.

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2007

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tom Vol. 28, nr 1-4

69-74

EN

Determination of optimal machining parameters is an engineering task with aim to reduce the production cost and achieve desired product quality. Such exercise can be tackled on many different ways. The goal of this work is to present some of the possible approaches and to benchmark them among each other. These principles are analyzed: response surface methodology (RSM), evolutionary algorithms (GA & GP), support vector regression (SVR) and artificial neural networks (ANN). All methods implement completely different data handling philosophies with the same goal, to build the model which is able to predict cutting force in satisfying manner. Those aspects are chosen to be evaluated and compared: average percentage deviation of all data, ability to find generalized model and minimize the risk of over fitting and at least the runtime of each single model determination. Average percentage deviation is one of the best indicators of the quality of model. The ability to find generalized model is good indicator of the flexibility of model, and shows how model deals with unknown data. The runtime is important in a real time environment or in scenarios where conditions change frequently. Cutting force data used in this benchmark comes from experimental research of longitudinal turning process.

11

Genetic modification, artificial individual, vaccine and serum - genetic engineering in evolutionary systems

41%

Gościniak I.

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2006

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tom z. 156

147-154

EN

The article shows the possibility of genetic engineering implementation in evolutionary systems as: genetic modification, artificial individual, vaccine and serum. In the article the group of immune systems is also proposed. Results of investigation presented in the article suggests that further development of genetic engineering in evolutionary systems will happen, thus raising their efficiency in problem solving.